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The development of advanced driver assistance systems (ADASs) will be a crucial element in the construction of future intelligent transportation systems with the objective of reducing the number of traffic accidents and their subsequent fatalities. Specifically, driving behaviors could be monitored online to determine the crash risk and provide warning information to the driver via their ADAS. In this paper, we focus on aggressiveness as one of the potential causes of traffic accidents. We demonstrate that aggressiveness can be detected by monitoring external driving signals such as lateral and longitudinal accelerations and speed. We model aggressiveness as a linear filter operating on these signals, thus scaling their probability distribution functions and modifying their mean value, standard deviation, and dynamic range. Next, we proceed to validate this model via an experiment, conducted under real driving conditions, involving ten different drivers, traveling a route with five different types of road sections, subject to both smooth and aggressive behaviors. The obtained results confirm the validity of the model of aggressiveness. In addition, they show the generality of this model and its applicability to specific driving signals (speed, longitudinal, and lateral accelerations), every single driver, and every road type. Finally, we build a classifier capable of detecting aggressive behavior from the driving signal. This classifier achieves a success rate up to 92%.

COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.